WO2022193794A1 - Multi-objective energy management method for smart community microgrid that takes into consideration decommissioned batteries - Google Patents

Abstract

Provided is a multi-objective energy management method for a smart community microgrid that takes into consideration decommissioned batteries, comprising the following steps: establishing a decommissioned battery remaining life decline model based on the number of charge and discharge cycles; establishing a multi-objective energy management model on the basis of energy consumption costs of community users, the decommissioned battery remaining life decline model, power consumption behaviors of the users, and effects of a residential community load on a power distribution system; recording decommissioned battery state information in a smart community; collecting power consumption information of the community users, and predicting a renewable energy output of the community; and according to the decommissioned battery state information and a renewable energy output prediction value of the current time period, solving the multi-objective energy management model by using the NSGA-III algorithm. Therefore, the actual problem that an energy storage battery is high in price and is thus difficult to be widely applied to a user side is solved, energy consumption costs of a community are reduced, and effects of an energy management system on power consumption behaviors of users are reduced.

Description

Ultra-multi-objective energy management method for smart community microgrid considering retired batteries technical field

The invention relates to a super-multi-target energy management method for an intelligent community micro-grid considering retired batteries.

Background technique

In recent years, with the continuous development of smart grid, smart communities have attracted widespread attention as energy use terminals. Smart communities support clean energy and energy storage batteries, encourage energy cascade utilization and recycling, and guide users to optimize energy consumption structure, improve energy efficiency, and achieve energy conservation and emission reduction. In order to realize real-time monitoring and optimal operation management of various integrated energy sources (electricity, heat, gas, and electric vehicles) in smart communities, and improve energy utilization efficiency, it is possible to install smart energy management controllers and smart meters in each household to establish The intelligent community energy management system reduces residential electricity costs, smoothes the load curve, improves the quality and safety of system power supply, and realizes friendly interaction between electricity consumption and household users.

There are three main types of energy management systems that have been announced so far, namely, a single household energy management system, a building energy management system and a community energy management system (CEMS). The single-family energy management system mainly optimizes the scheduling of loads with the goal of minimizing the user's electricity cost or maximizing the user's comfort. Compared with a single family, a smart community has the advantages of a large amount of dispatchable load, a large number of distributed power sources, and a great potential for coordination with the power grid. On the other hand, the price of energy storage batteries is still relatively high, and it is difficult to be widely used in the user field. According to statistics, tens of billions of retired batteries will enter the recycling market from 2020. It can be used in other fields with lower battery performance requirements, such as microgrid, backup power supply, lighting, etc. However, considering that retired batteries have more complex decay characteristics compared to ordinary battery life, it is necessary to incorporate them into energy management models for optimization. At present, the research on the energy management model of smart communities has just started, and there are few studies that consider the coordinated utilization of retired batteries and distributed energy. In addition, the community energy management system includes the coordination and interaction between the grid side and the residential users, which is reflected in the user's electricity cost, load curve, and user's electricity consumption behavior.

Therefore, it is very necessary to provide a super-multi-objective energy management model for smart community microgrids based on retired batteries.

SUMMARY OF THE INVENTION

In order to solve the above-mentioned technical problems, the present invention provides a super multi-target energy management method for an intelligent community microgrid considering retired batteries with simple algorithm and low cost.

The technical solution of the present invention to solve the above problem is: a super multi-target energy management method for an intelligent community microgrid considering retired batteries, which is characterized in that it includes the following steps:

Step 1: Based on the remaining available capacity of the retired battery, the number of remaining charge-discharge cycles and the capacity retention rate, a decay model of the remaining life of the retired battery based on the number of charge-discharge cycles is established;

Step 2: Comprehensively analyze the energy consumption behavior of each household, and determine the dispatchable range of community electric energy demand. Establish a super multi-objective energy management model;

Step 3: Record the retired battery status information in the smart community;

Step 4: Collect electricity consumption information of community users, and forecast the output of community renewable energy;

Step 5: Combined with the status information of retired batteries and the forecast value of renewable energy output in the current period, the NSGA-III algorithm is used to solve the super-multi-objective energy management model, and the charge/discharge amount of retired batteries in each period of the day is obtained. After adjustment The total energy consumption curve of the smart community.

In the above-mentioned intelligent community microgrid super-multi-target energy management method considering retired batteries, the specific process of step 1 is as follows:

The expected value of the annual cycle times of the electric vehicle power battery is calculated as:

In the formula: n _battery represents the annual number of charge and discharge cycles of the electric vehicle power battery, e is the power consumption per 100 kilometers of the electric vehicle; E(D) is the expected daily mileage of the electric vehicle, μ _D and σ _D are the daily mileage E respectively (D) mean value and variance, μ _D = 3.2, σ _D = 0.88; Q _battery represents the rated capacity of the power battery;

When the power battery is retired, the number of cycles n _retire has been calculated as:

n _retire = N·n _battery (3)

In the formula: N is the service life of the power battery when it is retired;

The ratio of the actual capacity to the rated capacity of the power battery is defined as the capacity retention rate; during the use period, the remaining capacity of the retired battery decreases with the increase of the number of charge and discharge cycles, and the decay law of the capacity retention rate with the number of charge and discharge cycles conforms to the power function relationship, which is expressed as :

Rc(n)=Q ₀ (C)-χ·n ^τ (4)

where Rc(n) is the capacity retention rate of the retired battery after n cycles; Q ₀ (C), χ and τ are the initial capacity retention rate, capacity decay coefficient and power exponent, respectively;

It is defined that when the capacity retention rate decays to the capacity retention rate threshold Rc ^thr , the retired battery will be scrapped. Therefore, the maximum available number of cycles n _scrap of the retired battery is expressed as:

The remaining number of charge-discharge cycles n _sec of the retired battery is obtained by subtracting the number of cycles from the maximum available cycles, and is calculated as:

n _sec =n _scrap -n _retire (6)

The cell capacity of the retired battery is defined as A _rate (mAh), and it is stipulated that the retired battery is used until the capacity retention rate decays to the threshold value Rc ^thr for scrap processing, and the available interval capacity A _SL of the retired battery is calculated as:

A _SL =A _rate ·[Rc(n ^retire )-Rc ^thr ] (7)

Combined with the remaining available capacity of the retired battery and the number of remaining charge-discharge cycles, the average decay capacity A _fade of a fully charged-discharge cycle of the retired battery is estimated as:

A _fade = A _SL /n _sec (8)

In the above-mentioned smart community microgrid super-multi-objective energy management method considering retired batteries, in the second step, the energy consumption behavior of each household is comprehensively analyzed, and the process of determining the schedulable interval of community electric energy demand is as follows:

The 24-hour time in a day is discretized and divided into T periods. For any t period, there is t∈[1,2,...,T]. At the beginning of a scheduling period, the The energy management center predicts the electricity load curve and renewable energy output information of community residents through the intelligent measurement system;

The photovoltaic output of the microgrid smart community is expressed as follows:

P _solar =A·S·ξ·[1-0.005(T _out -25)] (9)

In the formula: P _solar represents the photovoltaic output, S is the photovoltaic array area installed in the residential community, ξ is the photoelectric conversion efficiency, A is the light intensity, and T _out is the outdoor temperature;

For community users, let the set of households in the smart community be Θ, and the power consumption of the lth household in the past mth day period t is expressed as

Combined with historical data, the value range of residential electricity load is obtained.

In the above-mentioned method for super-multi-objective energy management of an intelligent community microgrid considering retired batteries, in the second step, the established super-multi-objective energy management model includes:

The objective function f ₁ with the goal of minimizing the total energy cost of the community is expressed as:

In the formula, Φ(t) represents the energy consumption cost function in time t, θ(t) and ρ(t) represent the electricity purchase price and electricity sales price from the smart community microgrid to the upper power grid, respectively; P _grid (t) represents the intelligent The interactive power between the community and the upper power grid, where P _grid (t) ≥ 0 means that the smart community purchases electricity from the external power grid, and vice versa means selling electricity;

The objective function f ₂ aiming at the least impact on the user's energy use behavior is expressed as:

In the formula, P _com (t) represents the total electricity load of the smart community in the period t before the optimization of the energy management system.

represents the total electricity load of the smart community in the period t after the optimization of the energy management system, P _home,l (t) is the load of the lth user in the smart community in the period t, and Θ represents the set of all users in the community;

The objective function _f3 with the goal of minimizing the life loss of retired batteries is expressed as:

In the formula, A _fade is the evaluation fade capacity of the retired battery after one full charge-discharge cycle,

Represents the equivalent full charge and discharge times per day after the decommissioned battery is converted, p is a constant, the value range is [0.8-2.1], C represents the set of charge/discharge half-cycles,

Represents the depth of discharge value of the battery in the kth half cycle, which is obtained from the energy curve of the retired battery. The calculation formula is shown in (14), where k is the index of the number of half cycles of the retired battery, and the total number of half cycles is the modulus value of C;

In the formula, E _SL,rate represents the rated capacity of the retired battery, E _k represents the energy level of the retired battery after the end of the kth half cycle, which corresponds to the local extreme point on the energy curve;

The objective function _f4 , which aims to minimize the peak-to-average ratio of the community load curve, is composed of the sum of the forward peak-to-average ratio and the reverse peak-to-average ratio. Compared with PPAR, the reverse peak-to-average ratio is the load peak-to-average ratio NPAR when the smart community sells electricity to the upper power grid, which is expressed as:

In the formula, _TN and _TP represent the electricity purchase time and electricity sale time respectively in a scheduling period.

In the above-mentioned super-multi-objective energy management method for an intelligent community microgrid considering retired batteries, the step 2 further includes setting constraints on the established super-multi-objective energy management model:

(1) System power balance constraints:

In the formula, P _solar (t) represents the photovoltaic output at time t, and P _SL (t) represents the charge/discharge power of the retired battery at time t. If P _SL (t)>0, it means that the retired battery is charged, otherwise it means discharge;

Represents the total electricity load of the smart community in the t period after the optimization of the energy management system;

(2) Community load curve constraints:

in,

Represents the minimum energy consumption power of the smart community period t;

represents the maximum energy consumption power of the microgrid community period t,

represents the total electricity load of the community in period t after energy management system optimization, and P _com (t) represents the total load of the community;

(3) Operational constraints of decommissioned battery energy storage systems:

In the formula: SOC _SL (t) represents the state of charge of the retired battery during t period, SOC _SL,min and SOC _SL,max represent the minimum state of charge and the maximum state of charge of the retired battery, respectively, P _SL,max- and P _{SL ,max+} are the maximum charging power and discharging power of the retired battery, respectively, SOC _desire represents the preset threshold of the state of charge of the retired battery, E _SL (t) represents the remaining power of the retired battery in the period t, and E _SL,rate represents the rated capacity of the retired battery .

The above-mentioned smart community microgrid ultra-multi-objective energy management method considering retired batteries, in the community load constraint in step 2, the total community load P _com (t) is calculated by the following method: using historical load data to estimate the community households in each The minimum load power and the maximum load power of time period t, and then sum up all household electricity loads to obtain the community load value range. The specific calculation method is as follows:

In the formula,

represents the power consumption of the lth household in the period t on the mth day in the past;

Respectively represent the maximum load power and minimum load power of the lth household in time period t; M represents the total number of days of sampled historical data.

In the above-mentioned intelligent community microgrid ultra-multi-objective energy management method considering retired batteries, in the operation constraints of the retired battery energy storage system in the second step, the specific calculation method of the state of charge value SOC _SL (t) of the retired batteries is as follows:

In the formula: when P _SL (t) is positive, it means the retired battery is charged, otherwise it means discharge, and η _c and η _d represent the battery charging efficiency and discharging efficiency, respectively.

In the above-mentioned ultra-multi-objective energy management method for an intelligent community microgrid considering retired batteries, in the second step, the ultra-multi-objective energy management model also includes a system transmission power constraint:

In the formula, P _grid,max+ and P _grid,max- represent the maximum forward transmission capacity and maximum negative transmission capacity of the transmission line between the smart community and the upper power grid, respectively, and T is the total dispatch period of the super-multi-objective energy management model.

The beneficial effects of the present invention are:

1. Based on the remaining available capacity of the retired battery, the number of remaining charge-discharge cycles and the capacity retention rate, the present invention establishes a decay model of the remaining life of the retired battery based on the number of charge-discharge cycles, which can effectively reduce the cost of the battery, prolong the service life of the battery, and maximize the performance of the battery. The residual value of retired batteries greatly reduces the cost of energy storage batteries involved in household energy management, and solves the practical problems of high prices of energy storage batteries and difficulty in wide application on the user side.

2. The ultra-multi-objective energy management model established by the present invention simultaneously considers factors such as decommissioned battery life loss, community energy consumption cost, community load curve peak-to-average ratio, and user energy consumption habits, and provides a feasible solution that takes into account the above four objectives. It is beneficial to comprehensively study the impact of the community energy management model on users and power grids from multiple aspects.

Description of drawings

FIG. 1 is a flow chart of the present invention.

Figure 2 is a schematic diagram of solving a super-multi-objective energy management model based on the NSGA-III algorithm.

Figure 3 is a schematic diagram of a retired battery energy curve.

Figure 4 is a graph of the total energy consumption of the smart community.

Figure 5 is a schematic diagram of typical sunlight intensity and ambient temperature.

FIG. 6 is a schematic diagram of the state of charge of retired batteries in typical sub-day scenarios 3 and 4.

FIG. 7 is a graph of community load under typical daytime scenarios 1 to 3.

Detailed ways

The present invention will be further described below with reference to the accompanying drawings and embodiments.

As shown in Figure 1, a smart community microgrid ultra-multi-objective energy management method considering retired batteries includes the following steps:

Step 1: Based on the remaining available capacity of the retired battery, the number of remaining charge-discharge cycles and the capacity retention rate, a decay model of the remaining life of the retired battery based on the number of charge-discharge cycles is established.

The expected value of the annual cycle times of the electric vehicle power battery is calculated as:

In the formula: n _battery represents the annual number of charge and discharge cycles of the electric vehicle power battery, e is the power consumption per 100 kilometers of the electric vehicle; E(D) is the expected mileage of the electric vehicle, μ _D , σ _D are the daily mileage E ( The mean and variance of D), μ _D = 3.2, σ _D = 0.88; Q _battery represents the rated capacity of the power battery.

When the power battery is retired, the number of cycles n _retire has been calculated as:

n _retire = N·n _battery (3)

In the formula: N is the service life of the power battery when it is retired.

The ratio of the actual capacity to the rated capacity of the power battery is defined as the capacity retention rate; during the use period, the remaining capacity of the retired battery decreases with the increase of the number of charge and discharge cycles, and the decay law of the capacity retention rate with the number of charge and discharge cycles conforms to the power function relationship, which is expressed as:

Rc(n)=Q ₀ (C)-χ·n ^τ (4)

where Rc(n) is the capacity retention rate of the retired battery after n cycles; Q ₀ (C), χ and τ are the initial capacity retention rate, capacity decay coefficient and power exponent, respectively.

In this embodiment, it is defined that when the capacity retention rate decays to the capacity retention rate threshold Rc ^thr , the retired battery is scrapped. Therefore, the maximum available number of cycles n _scrap of the retired battery is expressed as:

The remaining number of charge-discharge cycles n _sec of the retired battery is obtained by subtracting the number of cycles from the maximum available cycles, and is calculated as:

n _sec =n _scrap -n _retire (6)

The cell capacity of the retired battery is defined as A _rate (mAh), and it is stipulated that the retired battery is used until the capacity retention rate decays to the threshold value Rc ^thr for scrap processing, and the available interval capacity A _SL of the retired battery is calculated as:

A _SL =A _rate ·[Rc(N ^retire )-Rc ^thr ] (7)

Combined with the remaining available capacity of the retired battery and the number of remaining charge-discharge cycles, the average decay capacity A _fade of a fully charged-discharge cycle of the retired battery is estimated as:

A _fade = A _SL /n _sec (8)

Step 2: Comprehensively analyze the energy consumption behavior of each household, and determine the dispatchable range of community electric energy demand. Build a super-multi-objective energy management model.

By comprehensively analyzing the energy consumption behavior of each household, the process of determining the dispatchable interval of community electric energy demand is as follows:

The 24-hour time in a day is discretized and divided into T periods. For any t period, there is t∈[1,2,...,T]. When a new scheduling period begins, the intelligent community The internal energy management center predicts the electricity load curve and renewable energy output information of community residents through the intelligent measurement system.

The photovoltaic output of the microgrid smart community is expressed as follows:

P _solar =A·S·τ·[1-0.005(T _out -25)] (9)

In the formula: P _solar represents the photovoltaic output, S is the photovoltaic array area installed in the residential community, τ is the photoelectric conversion efficiency, A is the light intensity, and T _out is the outdoor temperature.

For community users, let the set of households in the smart community be Θ, and the power consumption of the lth household in the past mth day period t is expressed as

Combined with historical data, the value range of residential electricity load is obtained.

Ultra-multi-objective energy management models include:

The objective function f ₁ with the goal of minimizing the total energy cost of the community is expressed as:

In the formula, θ(t) and ρ(t) represent the electricity purchase price and electricity selling price from the smart community microgrid to the upper grid, respectively; P _grid (t) represents the interactive power between the smart community and the upper grid, where P _grid (t) ≥0 means that the smart community purchases electricity from the external power grid, and vice versa means selling electricity.

The objective function f ₂ aiming at the least impact on the user's energy use behavior is expressed as:

In the formula, P _com (t) represents the total electricity load of the smart community in the period t before the optimization of the energy management system.

represents the total electricity load of the smart community in the period t after the optimization of the energy management system, P _home,l (t) is the load of the lth user in the smart community in the period t, and Θ represents the set of all users in the community.

The objective function _f3 with the goal of minimizing the life loss of retired batteries is expressed as:

In the formula, A _fade is the average decay capacity of a fully charged and discharged battery once fully charged and discharged,

Represents the equivalent full charge and discharge times per day after the decommissioned battery is converted, p is a constant, the value range is [0.8-2.1] C represents the set of charge/discharge half-cycles,

Represents the depth of discharge value of the battery in the kth half cycle, which is obtained from the energy curve of the retired battery. The calculation formula is shown in (14), where k is the index of the number of half cycles of the retired battery, and the total number of half cycles is the modulus value of C;

In the formula, _ESL,rate represents the rated capacity of the retired battery, and _Ek represents the energy level of the retired battery after the end of the kth half cycle, which corresponds to the local extreme point on the energy curve, as shown in Figure 2.

The objective function _f4 , which aims to minimize the peak-to-average ratio of the community load curve, is composed of the sum of the forward peak-to-average ratio and the reverse peak-to-average ratio. Compared with PPAR, the reverse peak-to-average ratio is the load peak-to-average ratio NPAR when the smart community sells electricity to the upper power grid, which is expressed as:

In the formula, _TN and _TP respectively represent the power purchase time and the power sales time in a dispatch cycle.

Set constraints on the established super-multi-objective energy management model:

(1) System power balance constraints:

In the formula, P _solar (t) represents the photovoltaic output at time t, and P _SL (t) represents the charge/discharge power of the retired battery at time t. If P _SL (t)>0, it means that the retired battery is charged, otherwise it means discharge;

Represents the total electricity load of the smart community in the t period after optimization.

(2) Community load curve constraints:

in,

Represents the minimum energy consumption power of the smart community period t;

represents the maximum energy consumption power of the microgrid community period t,

represents the total electricity load of the smart community in the period t after the optimization of the energy management system, and P _com (t) represents the total load of the community.

The total community load P _com (t) can be calculated by the following method: using historical load data to estimate the minimum load power and maximum load power of community households in each time period t, and then summing all household electrical loads to obtain the community load value The specific calculation method is as follows:

In the formula,

represents the power consumption of the lth household in the period t on the mth day in the past;

Respectively represent the maximum load power and minimum load power of the lth household in time period t; M represents the total number of days of sampled historical data.

(3) Operational constraints of decommissioned battery energy storage systems:

In the formula: SOC _SL (t) represents the state of charge of the retired battery during t period, SOC _SL,min and SOC _SL,max represent the minimum state of charge and the maximum state of charge of the retired battery, respectively, P _SL,max- and P _{SL ,max+} are the maximum charging power and discharging power of the retired battery, respectively, SOC _desire represents the preset threshold of the state of charge of the retired battery, E _SL (t) represents the remaining power of the retired battery in the period t, and E _SL,rate represents the rated energy of the retired battery capacity.

The specific calculation method of the state of charge value SOC _SL (t) of the retired battery is as follows:

In the formula: when P _SL (t) is positive, it means the retired battery is charged, otherwise it means discharge, and η _c and η _d represent the battery charging efficiency and discharging efficiency, respectively.

(4) System transmission power constraints:

In the formula, P _grid,max+ and P _grid,max- represent the maximum forward transmission capacity and maximum negative transmission capacity of the transmission line between the smart community and the upper power grid, respectively, and T is the total dispatch period of the super-multi-objective energy management model.

Step 3: Record the retired battery status information in the smart community.

Step 4: Collect electricity consumption information of community users and forecast the output of community renewable energy.

Step 5: Combined with the status information of retired batteries and the predicted value of renewable energy output in the current period, the NSGA-III algorithm is used to solve the super multi-objective energy management model, and the charge/discharge amount of retired batteries in each period of the day is obtained. After adjustment The total energy consumption curve of the smart community.

In the embodiment, the predicted value of the uncertain variable includes the photovoltaic output P _solar (t) and the intelligent community resident load P _com (t), and the solver uses the intelligent algorithm NSGA-III to solve the problem. The specific solving steps are known to those skilled in the art, and details are not described in this embodiment of the present invention.

In order to make those skilled in the art better understand, the present invention takes a small intelligent community as an example to carry out numerical simulation.

In the embodiment, the smart community consists of 50 households, and the load data of each resident user in the past 90 days is selected to obtain the upper and lower limits of the electric power consumption of each resident user. On this basis, the total load curve and load curve interval of the smart community can be obtained, as shown in Figure 3.

This embodiment assumes that community residents share distributed photovoltaic power generation units, the area of photovoltaic arrays in smart communities is 640 square meters, and the photoelectric conversion efficiency is 16.4%. The light intensity curve and temperature data of a typical day are shown in Figure 4. Assuming that the working temperature of the retired battery is maintained at a constant temperature of 27 degrees Celsius, Table 1 shows the specific parameter configuration of the retired battery, and Table 2 shows the time-of-use electricity price data of the upper-level power grid. The transmission power limit with the upper power grid is 400kw. In this scheduling model, the total scheduling period T is 24 hours, the time interval is 30 minutes, and the total scheduling period is 48. Suppose a resident user starts a normal day at 7:00 am, so we set the start time of the scheduling period as 7:00 am.

Table 1 Operating parameters of decommissioned batteries

Table 2 hourly electricity price

In order to verify the effectiveness of the super-multi-objective energy management method in this embodiment, the following four different scenarios are simulated and analyzed:

Case 1: Retirement batteries and distributed photovoltaics are not considered, and energy management is not performed in the community. The energy cost of the community is calculated according to the time-of-use electricity price and the total electricity load;

Case 2: Multi-objective energy management for the community regardless of retired batteries and distributed photovoltaics (optimization goal 1: minimize energy cost; optimization goal 2: minimize interference to user behavior; optimization goal 3: minimize community load curve peak-to-average ratio);

Case 3: Considering retired batteries and distributed photovoltaics, a smart community super-multi-objective energy management method based on retired batteries;

Case 4: Considering retired batteries and distributed photovoltaics, on the basis of Case 3, the objective function does not include the capacity decay cost f3 of retired batteries.

The total energy cost, user behavior impact, retired battery life decay cost, and peak-to-average ratio under the four scenarios are shown in Table 3.

Table 3 Comparison of optimization target values in different scenarios

Taking Case 1 as the benchmark scenario for analysis and comparison, it can be seen from Table 3 that although Case 2 has implemented multi-objective energy management for the community, by optimizing the total load curve of the smart community, the total energy consumption cost and positive peak are reduced to a certain extent. The average ratio, but to a large extent, has an impact on the user's electricity consumption behavior. The impact value of electricity consumption behavior of residents in the entire community reached 814.60kW, and the average electricity consumption behavior impact of each household was about 16.292kW. Compared with Case1, the total energy cost and the positive peak-to-average ratio of the community in Case3 are reduced to a certain extent. The energy cost (118.23$) is reduced by about 57.93%, and the positive peak-to-average ratio (1.77a.u.) is reduced by about 23.56 %. Compared with Case2, although the positive peak-to-average ratio of Case3 is slightly larger (exceeding Case2 by 21.23%), both the community energy cost f1 and the user's electricity behavior impact f2 are significantly reduced, reducing by 49.69% and 67.94%, respectively. To sum up, compared with Case1 and Case2, Case3 can comprehensively consider the impact on users' electricity consumption behavior, energy consumption cost and load peak-to-average ratio. In order to illustrate the impact of considering retired batteries on the household energy management model, Case3 and Case4 are further compared and analyzed. It can be found that the objective function does not include the capacity attenuation cost of retired batteries, which can reduce the energy cost of the community to a certain extent and alleviate the impact on the community. The influence of the user's power consumption behavior and the reduction of the peak-to-average ratio of the load curve, but the frequent use of retired batteries will cause a large decline in the capacity of retired batteries. Case4 is about 56.15% more expensive than Case3 decommissioning battery life loss. In order to further illustrate the influence of different target models on the operation mode of retired batteries, Figure 5 shows the state of charge curves of retired batteries in two scenarios in one scheduling period. Compared with Case3, retired batteries in Case4 have experienced more charge and discharge frequencies. and deeper charge-discharge depth. Therefore, the cost of decommissioning battery life is greater for the same typical day

To further illustrate the effectiveness of the super-multi-objective energy management method, Fig. 6 presents the community load curves of Case1-Case3 under typical sun. Compared with Case 1, the Case 2 community reduced the electricity purchase from the upper power grid during the peak electricity price period (14:00-20:00pm), and increased the electricity purchased during the valley electricity price period (22:00pm-7:00am), reducing the total amount of electricity in the community. energy cost. Further, on the basis of Case2, Case3 not only increases the total electricity purchased during the valley electricity price period, but also uses photovoltaic power generation to reduce the electricity purchased during the flat electricity price period (7am-14pm, 20pm-22pm) to a certain extent. On the other hand, during the peak electricity price period (17:00pm-20:00pm), Case 3 purchases more electricity from the outside world than Case 2, and its purpose is to reduce the impact of the energy management model on the electricity consumption behavior of community residents. Therefore, the total energy cost under Case 3 is the smallest. By comparing Case 1, it can be found that the total community load curve of Case 3 is closer to Case 1, while the total community load curve of Case 2 is quite different from that of Case 1, which means that Case 3 has less influence on residents' electricity consumption behavior.

In order to further illustrate the impact of the super-multi-objective energy management model on the electricity cost of a single household, Table 4 shows the energy consumption costs of 50 households in the smart community in two different scenarios, Case1 and Case3. It can be clearly seen that under Case3, the electricity cost of each household can be greatly reduced. Compared with Case 1, the energy cost of some households under Case 3 is negative, which means that the household's share of electricity sales revenue exceeds its own electricity consumption cost. This is because the household sells photovoltaics to the upper power grid during the peak electricity price period. The generated electricity has obtained a large profit from the sale of electricity.

Table 4 Comparison of energy costs between scenarios 1 and 3 of each household user

To sum up, the super-multi-objective smart community energy management method proposed by the present invention can provide smart communities with more economical, clean, efficient and comfortable energy services by combining decommissioned batteries with distributed energy generation technology. At the same time, by properly designing a super-multi-objective energy management strategy, different operating objectives in the smart community can be well balanced, and the energy consumption cost of the user can be reduced as much as possible on the premise of ensuring that the user's electricity consumption behavior is not disturbed and less disturbed. Smooth the user load curve and prolong the service life of retired batteries. Meet the characteristics of intelligent community with high efficiency, environmental protection, energy saving and low carbon.

The above are only preferred embodiments of the present invention, and do not limit the present invention in any form. Although the present invention has been disclosed above with preferred embodiments, it is not intended to limit the present invention. Therefore, any simple modifications, equivalent changes and modifications made to the above embodiments according to the technical essence of the present invention without departing from the content of the technical solutions of the present invention should fall within the protection scope of the technical solutions of the present invention.

Claims (8)

1. A super-multi-target energy management method for an intelligent community microgrid considering retired batteries, characterized in that it includes the following steps:

   Step 1: Based on the remaining available capacity of the retired battery, the number of remaining charge-discharge cycles and the capacity retention rate, a decay model of the remaining life of the retired battery based on the number of charge-discharge cycles is established;

   Step 2: Comprehensively analyze the energy consumption behavior of each household, determine the dispatchable interval of community electric energy demand, based on the energy consumption cost of community users, the remaining life decline model of retired batteries, user electricity consumption behavior and the impact of residential community load on the power distribution system, Establish a super multi-objective energy management model;

   Step 3: Record the retired battery status information in the smart community;

   Step 4: Collect electricity consumption information of community users, and forecast the output of community renewable energy;

   Step 5: Combined with the status information of retired batteries and the predicted value of renewable energy output in the current period, the NSGA-III algorithm is used to solve the super multi-objective energy management model, and the charge/discharge amount of retired batteries in each period of the day is obtained. After adjustment The total energy consumption curve of the smart community.
2. The ultra-multi-target energy management method for an intelligent community microgrid considering retired batteries according to claim 1, wherein the specific process of the first step is:

   The expected value of the annual cycle times of the electric vehicle power battery is calculated as:

   

   

   In the formula: n _battery represents the annual number of charge and discharge cycles of the electric vehicle power battery, e is the power consumption per 100 kilometers of the electric vehicle; E(D) is the expected daily mileage of the electric vehicle, μ _D and σ _D are the daily mileage E respectively (D) mean value and variance, μ _D = 3.2, σ _D = 0.88; Q _battery represents the rated capacity of the power battery;

   When the power battery is retired from the electric vehicle, the number of cycles n _retire has been calculated as:

   n _retire = N·n _battery (3)

   In the formula: N is the service life of the power battery when it is retired;

   The ratio of the actual capacity to the rated capacity of the power battery is defined as the capacity retention rate; during the use period, the remaining capacity of the retired battery decreases with the increase of the number of charge and discharge cycles, and the decay law of the capacity retention rate with the number of charge and discharge cycles conforms to the power function relationship, which is expressed as :

   Rc(n)=Q ₀ (C)-χ·n ^τ (4)

   where Rc(n) is the capacity retention rate of the retired battery after n cycles; Q ₀ (C), χ and τ are the initial capacity retention rate, capacity decay coefficient and power exponent, respectively;

   It is defined that when the capacity retention rate decays to the capacity retention rate threshold Rc ^thr , the retired battery will be scrapped. Therefore, the maximum available number of cycles n _scrap of the retired battery is expressed as:

   

   The remaining number of charge-discharge cycles n _sec of the retired battery is obtained by subtracting the number of cycles from the maximum available cycles, and is calculated as:

   n _sec =n _scrap -n _retire (6)

   The cell capacity of the retired battery is defined as A _rate (mAh), and it is stipulated that the retired battery is used until the capacity retention rate decays to the threshold value Rc ^thr for scrap processing, and the available interval capacity A _SL of the retired battery is calculated as:

   A _SL =A _rate ·[Rc(n ^retire )-Rc ^thr ] (7)

   Combined with the remaining available capacity of the retired battery and the number of remaining charge-discharge cycles, the average decay capacity A _fade of a fully charged-discharge cycle of the retired battery is estimated as:

   A _fade = A _SL /n _sec (8)
3. The method for super-multi-objective energy management in an intelligent community microgrid considering retired batteries according to claim 2, wherein in the second step, the energy consumption behavior of each household is comprehensively analyzed to determine the schedulable interval of the community electric energy demand. The process is:

   The 24-hour time in a day is discretized and divided into T periods. For any t period, there is t∈[1,2,...,T]. At the beginning of a scheduling period, the The energy management center predicts the electricity load curve and renewable energy output information of community residents through the intelligent measurement system;

   The photovoltaic output of the microgrid smart community is expressed as follows:

   P _solar =A·S·ξ·[1-0.005(T _out -25)] (9)

   In the formula: P _solar represents the photovoltaic output, S is the photovoltaic array area installed in the residential community, ξ is the photoelectric conversion efficiency, A is the light intensity, and T _out is the outdoor temperature;

   For community users, let the set of households in the smart community be Θ, and the power consumption of the lth household in the past mth day period t is expressed as

   

   Combined with historical data, the value range of residential electricity load can be obtained.
4. The super-multi-target energy management method for an intelligent community microgrid considering retired batteries according to claim 3, wherein in the second step, the established super-multi-target energy management model includes:

   The objective function f ₁ with the goal of minimizing the total energy cost of the community is expressed as:

   

   In the formula, Φ(t) represents the energy consumption cost function in time t, θ(t) and ρ(t) represent the electricity purchase price and electricity sales price from the smart community microgrid to the upper power grid, respectively; P _grid (t) represents the intelligent The interactive power between the community and the upper power grid, where P _grid (t) ≥ 0 means that the smart community purchases electricity from the external power grid, and vice versa means selling electricity;

   The objective function f ₂ aiming at the least impact on the user's energy use behavior is expressed as:

   

   

   In the formula, P _com (t) represents the total electricity load of the smart community in the period t before the optimization of the energy management system.

   

   represents the total electricity load of the smart community in the period t after the optimization of the energy management system, P _home,l (t) is the load of the lth household in the smart community in the period t, and Θ represents the set of all households in the community;

   The objective function _f3 with the goal of minimizing the life loss of retired batteries is expressed as:

   

   In the formula, A _fade is the average decay capacity of a fully charged and discharged battery once fully charged and discharged,

   

   Represents the equivalent full charge and discharge times per day after the decommissioned battery is converted, p is a constant, the value range is [0.8-2.1], C represents the set of charge/discharge half-cycles,

   

   Represents the depth of discharge value of the battery in the kth half cycle, which is obtained from the energy curve of the retired battery. The calculation formula is shown in (14), where k is the index of the number of half cycles of the retired battery, and the total number of half cycles is the modulus value of C;

   

   In the formula, E _SL,rate represents the rated capacity of the retired battery, E _k represents the energy level of the retired battery after the end of the kth half cycle, which corresponds to the local extreme point on the energy curve;

   The objective function _f4 , which aims to minimize the peak-to-average ratio of the community load curve, is composed of the sum of the forward peak-to-average ratio and the reverse peak-to-average ratio. Compared with PPAR, the reverse peak-to-average ratio is the load peak-to-average ratio NPAR when the smart community sells electricity to the upper power grid, which is expressed as:

   

   In the formula, _TN and _TP represent the electricity purchase time and electricity sale time respectively in a scheduling period.
5. The ultra-multi-objective energy management method for an intelligent community microgrid considering retired batteries according to claim 4, characterized in that, in the step 2, it also includes setting constraints on the established ultra-multi-objective energy management model:

   (1) System power balance constraints:

   

   In the formula, P _solar (t) represents the photovoltaic output at time t, and P _SL (t) represents the charge/discharge power of the retired battery at time t. If P _SL (t)>0, it means that the retired battery is charged, otherwise it means discharge;

   

   Represents the total electricity load of the smart community in the t period after the optimization of the energy management system;

   (2) Community load curve constraints:

   

   in,

   

   Represents the minimum energy consumption power of the smart community period t;

   

   represents the maximum energy consumption power of the microgrid community period t,

   

   represents the total electricity load of the community in period t after energy management system optimization, and P _com (t) represents the total load of the community;

   (3) Operational constraints of decommissioned battery energy storage systems:

   

   In the formula: SOC _SL (t) represents the state of charge of the retired battery during t period, SOC _SL,min and SOC _SL,max represent the minimum state of charge and the maximum state of charge of the retired battery, respectively, P _SL,max- and P _{SL ,max+} are the maximum charging power and discharging power of the retired battery, respectively, SOC _desire represents the preset threshold of the state of charge of the retired battery, E _SL (t) represents the remaining power of the retired battery in the period t, and E _SL,rate represents the rated capacity of the retired battery .
6. The ultra-multi-objective energy management method for an intelligent community microgrid considering retired batteries according to claim 5, characterized in that, in the community load constraint in the second step, the total community load P _com (t) is calculated by the following method: Use historical load data to estimate the minimum load power and maximum load power of community households in each time period t, and then sum up all household electrical loads to obtain the range of community load values. The specific calculation method is as follows:

   

   

   In the formula,

   

   represents the power consumption of the lth household in the period t on the mth day in the past;

   

   

   Respectively represent the maximum load power and minimum load power of the lth household in time period t; M represents the total number of days of sampled historical data.
7. The super-multi-objective energy management method for an intelligent community microgrid considering retired batteries according to claim 6, characterized in that, in the operation constraints of the retired battery energy storage system in the second step, the state of charge value SOC _SL ( t) The specific calculation method is as follows:

   

   In the formula: when P _SL (t) is positive, it means the retired battery is charged, otherwise it means discharge, and η _c and η _d represent the battery charging efficiency and discharging efficiency, respectively.
8. The super-multi-objective energy management method for an intelligent community microgrid considering retired batteries according to claim 7, wherein in the second step, the super-multi-objective energy management model further includes a system transmission power constraint:

   

   In the formula, P _grid,max+ and P _grid,max- represent the maximum forward transmission capacity and maximum negative transmission capacity of the transmission line between the smart community and the upper power grid, respectively, and T is the total dispatch period of the super-multi-objective energy management model.

Images